25-year analysis of gender and professional trends in authorship of highly cited articles in leading ophthalmology journals
Bibliographic record
Abstract
Purpose Understanding trends in female researcher representation can inform strategies to improve equity in academic ophthalmology. This study evaluates authorship and citation trends over 25 years amongst the highest-cited articles in leading ophthalmology journals to assess female co-authorship and differences in authors’ degrees in publications at a highly visible research level. Methods A retrospective analysis was conducted on 443 authors who published the most highly cited articles in high-impact journals from 2000 to 2024. Articles were chosen as the three most cited articles per year from the three highest-impact ophthalmology journals per h5-index. Identities were confirmed via institutional websites, PubMed, and Google Scholar. Gender and degrees were determined using professional profiles. Outcomes included authorship position, first-last author pairings, citation impact, and differences in authors’ degrees by gender. Results From 2000 to 2024, female first authorship rose 38.11%, and overall authorship 43.73%. Female last authorship, though lowest, increased the most (53.63%). Female co-authorship and overall representation peaked in 2010–2014. Male first-authored papers had more citations ( p = 0.011) and were 9.36 times more likely to involve same-gender mentors (CI [4.56, 19.23], p < 0.00001). Men were more often ophthalmologists; women more frequently held PhDs, PharmDs, or ODs ( p = 0.0036). Conclusions Female authorship is rising, but citation gaps and underrepresentation persist. Non-physician female research and female co-authorship success suggest mentorship and interdisciplinary work may enhance women's visibility in ophthalmology research. Continued interventions to promote female ophthalmic career networks are essential in closing the gender gap in research and fostering equitable professional advancement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".